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Record W4392601386 · doi:10.3390/agriculture14030445

Trends in Soil Science over the Past Three Decades (1992–2022) Based on the Scientometric Analysis of 39 Soil Science Journals

2024· article· en· W4392601386 on OpenAlexaff
Lang Jia, Wenjuan Wang, Francis Zvomuya, Hailong He

Bibliographic record

VenueAgriculture · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCitationPopularityLibrary sciencePolitical scienceComputer science

Abstract

fetched live from OpenAlex

As one of the basic disciplines of agricultural, natural resource, and environmental science, soil science has played a critical role in global food security and socio-economic and ecological sustainability. The number of soil science journals and publications has increased remarkably with the development of soil science. However, there is a lack of systematic and comprehensive studies on the developmental trends of soil science based on journals and publications. In this study, 39 journals included under the soil science category in the 2022 Journal Citation Reports, and 112,911 publications in these journals from 1992 to 2022 were subjected to scientometric/bibliometric analysis to determine trends in publication, journal metrics, co-authorship, and research topics, in addition to general journal information. The results show that soil science ushered in a renaissance period with the number of publications, citations, impact factors, and CiteScore demonstrating an increasing trend. America and the Chinese Academy of Sciences had the most publications and citations. The most productive author published more than 400 articles. Soil science research focused mostly on its fundamental impact on the ecological environment based on the strongest citation bursts analysis of keywords. The analysis indicated that open access has increased in popularity. Current soil science journals still face a few common challenges, including an urgent need for a fairer evaluation mechanism on journal quality compared to the traditional use of single metrics as well as equity, diversity, and inclusion (EDI) in the whole editorial process. Artificial intelligence may bring new tools and more changes to the development of soil science. This study will help soil science researchers to better understand the development status and future trends of soil science. It will also guide authors in journal selection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0340.070
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.271
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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